library(tidyverse) # for graphing and data cleaning
library(googlesheets4) # for reading googlesheet data
library(lubridate) # for date manipulation
library(ggthemes) # for even more plotting themes
library(geofacet) # for special faceting with US map layout
gs4_deauth() # To not have to authorize each time you knit.
theme_set(theme_minimal()) # My favorite ggplot() theme :)
#Lisa's garden data
garden_harvest <- read_sheet("https://docs.google.com/spreadsheets/d/1DekSazCzKqPS2jnGhKue7tLxRU3GVL1oxi-4bEM5IWw/edit?usp=sharing") %>%
mutate(date = ymd(date))
# Seeds/plants (and other garden supply) costs
supply_costs <- read_sheet("https://docs.google.com/spreadsheets/d/1dPVHwZgR9BxpigbHLnA0U99TtVHHQtUzNB9UR0wvb7o/edit?usp=sharing",
col_types = "ccccnn")
# Planting dates and locations
plant_date_loc <- read_sheet("https://docs.google.com/spreadsheets/d/11YH0NtXQTncQbUse5wOsTtLSKAiNogjUA21jnX5Pnl4/edit?usp=sharing",
col_types = "cccnDlc")%>%
mutate(date = ymd(date))
## Error: Client error: (429) RESOURCE_EXHAUSTED
## * Either out of resource quota or reaching rate limiting. The client should look for google.rpc.QuotaFailure error detail for more information.
## * Quota exceeded for quota group 'ReadGroup' and limit 'Read requests per 100 seconds' of service 'sheets.googleapis.com' for consumer 'project_number:603366585132'.
##
## Error details:
## * Error details of type 'google.rpc.Help' are not implemented yet.
## * Workaround: use `tryCatch()` and inspect error payload yourself.
## * Please open an issue at https://github.com/r-lib/gargle/issues, so we can fix.
# Tidy Tuesday data
kids <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-15/kids.csv')
Before starting your assignment, you need to get yourself set up on GitHub and make sure GitHub is connected to R Studio. To do that, you should read the instruction (through the “Cloning a repo” section) and watch the video here. Then, do the following (if you get stuck on a step, don’t worry, I will help! You can always get started on the homework and we can figure out the GitHub piece later):
keep_md: TRUE in the YAML heading. The .md file is a markdown (NOT R Markdown) file that is an interim step to creating the html file. They are displayed fairly nicely in GitHub, so we want to keep it and look at it there. Click the boxes next to these two files, commit changes (remember to include a commit message), and push them (green up arrow).Put your name at the top of the document.
For ALL graphs, you should include appropriate labels.
Feel free to change the default theme, which I currently have set to theme_minimal().
Use good coding practice. Read the short sections on good code with pipes and ggplot2. This is part of your grade!
When you are finished with ALL the exercises, uncomment the options at the top so your document looks nicer. Don’t do it before then, or else you might miss some important warnings and messages.
These exercises will reiterate what you learned in the “Expanding the data wrangling toolkit” tutorial. If you haven’t gone through the tutorial yet, you should do that first.
garden_harvest data to find the total harvest weight in pounds for each vegetable and day of week. Display the results so that the vegetables are rows but the days of the week are columns.garden_harvest %>%
mutate(day = wday(date, label = TRUE)) %>%
group_by(vegetable, day) %>%
summarize(total_wt = sum(weight)) %>%
pivot_wider(names_from = day,
values_from = total_wt)
garden_harvest data to find the total harvest in pound for each vegetable variety and then try adding the plot variable from the plant_date_loc table. This will not turn out perfectly. What is the problem? How might you fix it?garden_harvest %>%
group_by(vegetable, variety) %>%
summarize(tot_harvest_lb = weight*0.0022) %>%
left_join(plant_date_loc,
by = c("vegetable", "variety"))
## Error in is.data.frame(y): object 'plant_date_loc' not found
Not every vegetable in the garden harvest data set has information on plot location. To get rid of any NA values, we could use an inner join function.
garden_harvest and supply_cost datasets, along with data from somewhere like this to answer this question. You can answer this in words, referencing various join functions. You don’t need R code but could provide some if it’s helpful.This could be accomplished by calculating how many seeds/supplies were used for each vegetable and variety in the garden harvest data set in conjunction with the prices themselves through the data in the supply costs data set. An inner join by vegetable, variety, would show us price of the specific supplies used.
garden_harvest %>%
filter(vegetable == "tomatoes") %>%
mutate(variety = fct_reorder(variety, date, min)) %>%
group_by(variety) %>%
summarize(tot_harvest_lb = sum(weight*0.0022),
min_date = min(date)) %>%
ggplot(aes(x = tot_harvest_lb, y = fct_rev(variety))) +
geom_col(fill = "tomato4")+
labs(title = "Tomato Varieties and Respective Harvest Weight
From Earliest to Latest First Harvest Date",
y = "",
x = "total pounds")
garden_harvest data, create two new variables: one that makes the varieties lowercase and another that finds the length of the variety name. Arrange the data by vegetable and length of variety name (smallest to largest), with one row for each vegetable variety. HINT: use str_to_lower(), str_length(), and distinct().garden_harvest %>%
mutate(lowercase = str_to_lower(variety),
length = str_length(variety)) %>%
group_by(vegetable, variety) %>%
summarize(length = mean(length)) %>%
arrange(vegetable, length)
garden_harvest data, find all distinct vegetable varieties that have “er” or “ar” in their name. HINT: str_detect() with an “or” statement (use the | for “or”) and distinct().garden_harvest %>%
mutate(has_er_ar = str_detect(variety, "er|ar")) %>%
filter(has_er_ar == TRUE) %>%
distinct(vegetable, variety)
In this activity, you’ll examine some factors that may influence the use of bicycles in a bike-renting program. The data come from Washington, DC and cover the last quarter of 2014.
{300px}
{300px}
Two data tables are available:
Trips contains records of individual rentalsStations gives the locations of the bike rental stationsHere is the code to read in the data. We do this a little differently than usualy, which is why it is included here rather than at the top of this file. To avoid repeatedly re-reading the files, start the data import chunk with {r cache = TRUE} rather than the usual {r}.
data_site <-
"https://www.macalester.edu/~dshuman1/data/112/2014-Q4-Trips-History-Data.rds"
Trips <- readRDS(gzcon(url(data_site)))
Stations<-read_csv("http://www.macalester.edu/~dshuman1/data/112/DC-Stations.csv")
NOTE: The Trips data table is a random subset of 10,000 trips from the full quarterly data. Start with this small data table to develop your analysis commands. When you have this working well, you should access the full data set of more than 600,000 events by removing -Small from the name of the data_site.
It’s natural to expect that bikes are rented more at some times of day, some days of the week, some months of the year than others. The variable sdate gives the time (including the date) that the rental started. Make the following plots and interpret them:
sdate. Use geom_density().Trips %>%
ggplot(aes(x = sdate))+
geom_density()+
labs(title = "Distribution of Bike Rentals by Date",
x = "",
y = "")
This density plot illustrates the distribution of bike rentals as time progresses. A majority of bike rentals occur in October and November, as weather is more permitting for a bike ride in these months. Likewise, in December and January when there is snow and cold weather, bike rentals are generally down.
mutate() with lubridate’s hour() and minute() functions to extract the hour of the day and minute within the hour from sdate. Hint: A minute is 1/60 of an hour, so create a variable where 3:30 is 3.5 and 3:45 is 3.75.Trips %>%
mutate(hour = hour(sdate),
minute = minute(sdate),
time = (hour + (minute/60))) %>%
ggplot(aes(x = time))+
geom_density()+
labs(title = "Distribution of Bike Rentals by Time of Day",
x = "hour of the day",
y = "")
This density plot illustrates the distribution of bike rentals by time of day. This density curve is bimodal, where there is a spike in bike rentals around 8:30 AM, the approximate commute to work time, and around 5:30 PM, the approximate commute home time.
Trips %>%
mutate(wday = wday(sdate, label = TRUE)) %>%
ggplot(aes(y = fct_rev(wday)))+
geom_bar()+
labs(title = "Bike Rentals by Day of the Week",
x = "",
y = "")
This barplot shows bike rentals by day of the week. Generally, weekdays have more rentals than weekends, but weekdays and weekends themselves look similar. Bike rentals are most popular on Fridays.
Trips %>%
mutate(hour = hour(sdate),
minute = minute(sdate),
time = (hour + (minute/60)),
wday = wday(sdate, label = TRUE)) %>%
ggplot(aes(x = time))+
facet_wrap(vars(wday))+
geom_density()+
labs(title = "Distribution of Bike Rentals by Time of Day",
x = "",
y = "")
There is a pattern in the distribution of bike rentals by time of day. When looking at weekdays, the density curves are bimodal, where there is a spike in bike rentals around 8:30 AM, the approximate commute to work time, and around 5:30 PM, the approximate commute home time. The weekends have a pattern as well, but have one spike around midday, rather than two spikes like a week day does.
The variable client describes whether the renter is a regular user (level Registered) or has not joined the bike-rental organization (Causal). The next set of exercises investigate whether these two different categories of users show different rental behavior and how client interacts with the patterns you found in the previous exercises. Repeat the graphic from Exercise @ref(exr:exr-temp) (d) with the following changes:
fill aesthetic for geom_density() to the client variable. You should also set alpha = .5 for transparency and color=NA to suppress the outline of the density function.Trips %>%
mutate(hour = hour(sdate),
minute = minute(sdate),
time = (hour + (minute/60)),
wday = wday(sdate, label = TRUE)) %>%
ggplot(aes(x = time, fill = client))+
facet_wrap(vars(wday))+
geom_density(alpha = .5)+
labs(title = "Distribution of Bike Rentals by Time of Day
and Type of Client",
x = "",
y = "")
position = position_stack() to geom_density(). In your opinion, is this better or worse in terms of telling a story? What are the advantages/disadvantages of each?Trips %>%
mutate(hour = hour(sdate),
minute = minute(sdate),
time = (hour + (minute/60)),
wday = wday(sdate, label = TRUE)) %>%
ggplot(aes(x = time, fill = client))+
facet_wrap(vars(wday))+
geom_density(alpha = .5, position = position_stack())+
labs(title = "Distribution of Bike Rentals by Time of Day
and Type of Client",
x = "",
y = "")
In my opinion, this is much better in terms of telling a story. There is much more of a descrepcency between the types of clients and the last graph was simply a mess. The lack of overlap or blending gives us the opportunity to make stronger, definitive conclusions.
weekend which will be “weekend” if the day is Saturday or Sunday and “weekday” otherwise (HINT: use the ifelse() function and the wday() function from lubridate). Then, update the graph from the previous problem by faceting on the new weekend variable.Trips %>%
mutate(hour = hour(sdate),
minute = minute(sdate),
time = (hour + (minute/60)),
day_of_week = wday(sdate, label = TRUE),
type_day = ifelse(wday(sdate) %in% c(1,7), "weekend", "weekday")) %>%
ggplot(aes(x = time, fill = client))+
facet_wrap(vars(type_day))+
geom_density(alpha = .5, position = position_stack())+
labs(title = "Distribution of Bike Rentals by Time of Day, Type of Day,
and Type of Client",
x = "",
y = "")
client and fill with weekday. What information does this graph tell you that the previous didn’t? Is one graph better than the other?Stations to make a visualization of the total number of departures from each station in the Trips data. Use either color or size to show the variation in number of departures. We will improve this plot next week when we learn about maps!Trips %>%
count(sstation) %>%
inner_join(Stations,
by = c("sstation" = "name"))
Trips %>%
count(sstation) %>%
left_join(Stations,
by = c("sstations" = "name"))
## Error: Join columns must be present in data.
## x Problem with `sstations`.
mutate(prop_casual = mean(client == "Casual")) %>%
ggplot(aes(x = lat, y = long))+
geom_point()
## Error in mean(client == "Casual"): object 'client' not found
as_date(sdate) converts sdate from date-time format to date format.top_trip <- Trips %>%
mutate(sdate = as_date(sdate)) %>%
count(sstation, sdate) %>%
slice_max(n = 10, order_by = n, with_ties = FALSE)
top_trip
Trips %>%
mutate(sdate = as_date(sdate)) %>%
inner_join(top_trip, by = c("sstation", "sdate"))
Trips %>%
mutate(sdate = as_date(sdate)) %>%
inner_join(top_trip, by = c("sstation", "sdate")) %>%
mutate(total = sum(count), prop = count/total) %>%
pivot_wider(id_cols = day_of_week,
names_from = client,
values_from = prop)
## Error: Problem with `mutate()` input `total`.
## x invalid 'type' (closure) of argument
## i Input `total` is `sum(count)`.
DID YOU REMEMBER TO GO BACK AND CHANGE THIS SET OF EXERCISES TO THE LARGER DATASET? IF NOT, DO THAT NOW.
This problem uses the data from the Tidy Tuesday competition this week, kids. If you need to refresh your memory on the data, read about it here.
facet_geo(). The graphic won’t load below since it came from a location on my computer. So, you’ll have to reference the original html on the moodle page to see it.DID YOU REMEMBER TO UNCOMMENT THE OPTIONS AT THE TOP?